Field of the Invention
The invention relates to a method for magnetic resonance imaging and a control device for magnetic resonance imaging.
Description of the Prior Art
In a magnetic resonance system the body to be examined is normally exposed to a relatively high basic magnetic field, for example of 1.5, 3 or 7 Tesla, with the use of a basic field magnetic system. Following creation of the basic field, nuclei in the examination object align themselves with a non-vanishing nuclear magnetic dipole moment, frequently also called spin, along the field. This collective behavior of the spin system is described as macroscopic “magnetization”. Macroscopic magnetization is the vector sum of all microscopic magnetic moments in the object at a particular location. In addition to the basic field, a magnetic field gradient is generated by a gradient system, by means of which the magnetic resonance frequency (Larmor frequency) is determined at the respective location. Using a radio-frequency transmission system, radio-frequency excitation signals (RF pulses) are transmitted by suitable antenna devices, which results in the macroscopic “magnetization” being tilted by a defined flip angle with respect to the magnetic field lines of the basic magnetic field. If such an RF pulse acts on spins that are already excited, they may be tipped into a different angular position or even flipped back into an initial state in parallel to the basic magnetic field. When the excited nuclear spins are relaxed, radio-frequency signals, known as magnetic resonance signals, are resonantly emitted and are received by suitable receiving antennas (also called magnetic resonance coils or receiving coils). The received signals are then demodulated and digitized, and are further processed as so-called “raw data”. The acquisition of the magnetic resonance signals takes place in the spatial frequency domain, known as “k-space”, wherein during a measurement e.g. of a slice, data entry points in k-space are proceeded through chronologically along a “gradient trajectory” (also called a “k-space trajectory”) defined by the switching of the gradient pulses. In addition, the transmission of the RF pulses must be coordinated on a suitable chronological basis. The desired image data can ultimately be reconstructed from the raw data acquired in this way by a two-dimensional Fourier transformation following further processing steps that generally also depend on the method of acquisition. Alternatively, three-dimensional volumes can be excited on a defined basis in the interim and read out, the raw data being in turn sorted into three-dimensional k-space following further processing steps. A three-dimensional image data volume can then be reconstructed accordingly by a three-dimensional Fourier transformation.
Predefined pulse sequences, i.e. sequences of defined RF pulses and gradient pulses in different directions, and readout windows, are normally used to control a magnetic resonance, scanner during the measurement, the receiving antennas being switched in the meantime to receive and the magnetic resonance signals being received and processed. With the use of a so-called measuring protocol these sequences are parameterized in advance for a desired examination, for example a particular contrast of the calculated images. The measuring protocol can also contain other control data for the measurement. There are a large number of magnetic resonance sequence techniques in accordance with which pulse sequences can be structured. One of the major challenges for future development in magnetic resonance imaging (MR imaging) is to speed up magnetic resonance sequence techniques without having to make extensive compromises in respect of resolution, contrast and susceptibility to artifacts.
Clinical MR imaging at present is based almost exclusively on so-called Cartesian imaging, in which the k-space points (i.e. the scanning points in k-space at which raw data are acquired) lie on the grid points of a Cartesian grid or raster and are scanned row by row or column by column. Using so-called PAT (Parallel Acquisition Technique) parallel imaging methods it has been possible to speed up clinical MR imaging significantly. In parallel MR imaging data acquisition is abridged, in that some of the rows of the raster actually necessary to reconstruct a fold-free image are not acquired in k-space. These missing rows are added subsequently in k-space during image reconstruction (for example in GRAPPA=Generalized Autocalibrating Partially Parallel Acquisition Imaging) or the fold artifacts resulting from scanning at too low a frequency are removed in the image space (for example SENSE=Sensitivity Encoding). One prerequisite for being able to employ the parallel imaging methods is that the radio-frequency signals should be received using several receiving coils (antennas), wherein the spatial sensitivity of the individual receiving coils must be known. The spatial sensitivity of the receiving coils, also called coil sensitivity below, is calculated with the help of so-called coil calibration measurement data. The coil calibration measurement data must generally be scanned sufficiently. Since the sensitivities generally vary slowly in spatial terms, it is normally sufficient if the coil calibration measurement data has a low spatial resolution. In general the coil calibration measurement data must be measured anew for each patient.
There are essentially two clinically employed PAT imaging methods: the SENSE method and the GRAPPA method. In both methods, as already described, a prerequisite for correct image reconstruction is the performance of a calibration measurement. During the calibration measurement a second data set, the so-called “coil calibration data set”, is obtained. This coil calibration data set is scanned or measured in full (in other words sufficiently in accordance with Nyquist). The receive signals or magnetic resonance signals S1(t) obtained in this way are converted into raw data by demodulation and digitization, said raw data being used during a subsequent image reconstruction to replace the rows in k-space which are missing during the actual data acquisition.
The receive signal S1(t) is obtained for the 1-th receiving coil from the following signal equation:S1(t)=∫∫C1(r)·s(r,t)·e−iϕ(r,t)dr  (1)
Here s(r,t) represents the time-dependent magnitude of the MR signal from the location r in the examination region. Φ(r,t) represents the time-dependent phase of the MR signal from the location r. This phase depends on the signal history, e.g. on the phase encoding steps, and on the signal frequency, which is influenced for example by local gradient fields. C1(r) describes a complex location-dependent sensitivity profile of the 1-th receiving antenna coil.
In the SENSE method the sensitivities C1(r) of the receiving antenna coils are calculated directly from the calibration measurement data S1(t) using the method known to the person skilled in the art. The calibration in this case takes place in the image domain.
Using the calculated sensitivities C1(r), the fold artifacts resulting from scanning at a frequency that is too low are removed in the image space during the image reconstruction.
In the GRAPPA method the coil sensitivities C1(r) of the 1-th coil are not calculated directly, but are only taken into consideration implicitly. Interpolation weightings w1(kx,ky) are calculated from the calibration measurement data S1(t) acquired during the calibration measurement. The interpolation weightings w1(kx,ky) depend implicitly on the coil sensitivities C1(r). The rows in k-space that are missing (not filled) during the recording made at too low a frequency are added in k-space subsequently during the image reconstruction with the use of the interpolation weightings.
To determine the interpolation weightings w1(kx,ky), an equation system based on the data obtained during the calibration measurement is established. Generally so many scanning points are measured that the equation system is overdetermined. This equation system is then resolved using standard methods in the context of the smallest quadratic deviation.
With the use of the GRAPPA weightings, the missing raw data in k-space can be reconstructed, before the image data are reconstructed therefrom using the transformation in the image domain.
In the GRAPPA method, the coil sensitivities are therefore implicitly taken into consideration, by recording supplementary data around the center of k-space during the image recording and using this data to calibrate a uniform interpolation kernel, with which the missing data in k-space can be calculated.
However, in the conventional use of the aforementioned PAT methods, only a spatial dependency of the coil sensitivities C1(r) is taken into consideration. Until now the additional frequency dependency of the coil sensitivities on the local frequency ω(r) of the MR signal was not taken into consideration. The frequency dependency of the coil sensitivities is illustrated in detail in FIGS. 1 and 2.
PAT methods such as SENSE and GRAPPA have found a place in clinical MR imaging and supply images with a good image quality for Cartesian trajectories. However, neither method works robustly for non-Cartesian trajectories, such as radial or spiral trajectories for example. It was previously suspected that these problems occur as a result of unknown errors in the gradient trajectories. Attempts were made to calibrate the gradient trajectories in advance using dynamic field cameras for measuring magnetic fields and to make error corrections to the gradients. However, the problem still was not satisfactorily solved.